A theoretical model is developed for analysis of intervention effects in multiple autocorrelated time-series which may be classified or blocked two ways. The intervention effect is estimated independently for each series. The estimates are then entered as observations in two-factor analysis of variance with one observation per cell, utilizing a pooled variance error estimate for the mean square error. The model was tested empirically through the use of Monte Carlo techniques, and results are presented in tabular form for Type I and Type II error levels. An example is presented with data taken from a Kansas City Police study utilizing the two-factor layout. (Author)